What is Data Cleaning?
Raw data collected from forms, systems, or databases is almost never perfect. It contains errors, inconsistencies, duplicate records, and missing values that can lead to incorrect analysis and poor decisions.
Data cleaning (also called data wrangling or data munging) is the process of identifying and correcting these problems so your dataset is accurate, consistent, and ready to be analyzed.
What's Included
- Detection and removal of duplicate rows and records
- Handling missing values (imputation, removal, or flagging)
- Fixing data types (dates, numbers, strings)
- Standardizing text fields (capitalization, spacing, encoding)
- Outlier detection and treatment
- Reformatting columns and headers
- Merging or splitting columns as needed
- Delivery of a clean file + a summary of changes made
Process
Receive Your File
You upload your dataset through Upwork. I review the file and confirm the scope of work.
Audit & Diagnose
I run a full diagnostic to identify all data quality issues — duplicates, nulls, type errors, and inconsistencies.
Clean & Validate
Each issue is addressed systematically using Python (pandas) or Excel, with validation checks at every step.
Deliver Results
You receive the cleaned file plus a written summary documenting every change made and why.
Tools Used
Pricing
Pricing is based on file size and row count. All prices are starting points — exact pricing is confirmed after reviewing your file on Upwork.
- Duplicate removal
- Missing value handling
- Basic formatting fix
- Cleaned file delivery
- Everything in Basic
- Outlier detection
- Column restructuring
- Change summary report
- Everything in Standard
- Multi-file merging
- Complex transformations
- Full audit trail
- All Advanced features
- Database-level cleaning
- Automation scripts
- Priority delivery